Publications by authors named "Yuki Bradford"

40 Publications

Tissue specificity-aware TWAS (TSA-TWAS) framework identifies novel associations with metabolic, immunologic, and virologic traits in HIV-positive adults.

PLoS Genet 2021 Apr 26;17(4):e1009464. Epub 2021 Apr 26.

Department of Genetics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

As a type of relatively new methodology, the transcriptome-wide association study (TWAS) has gained interest due to capacity for gene-level association testing. However, the development of TWAS has outpaced statistical evaluation of TWAS gene prioritization performance. Current TWAS methods vary in underlying biological assumptions about tissue specificity of transcriptional regulatory mechanisms. In a previous study from our group, this may have affected whether TWAS methods better identified associations in single tissues versus multiple tissues. We therefore designed simulation analyses to examine how the interplay between particular TWAS methods and tissue specificity of gene expression affects power and type I error rates for gene prioritization. We found that cross-tissue identification of expression quantitative trait loci (eQTLs) improved TWAS power. Single-tissue TWAS (i.e., PrediXcan) had robust power to identify genes expressed in single tissues, but, often found significant associations in the wrong tissues as well (therefore had high false positive rates). Cross-tissue TWAS (i.e., UTMOST) had overall equal or greater power and controlled type I error rates for genes expressed in multiple tissues. Based on these simulation results, we applied a tissue specificity-aware TWAS (TSA-TWAS) analytic framework to look for gene-based associations with pre-treatment laboratory values from AIDS Clinical Trial Group (ACTG) studies. We replicated several proof-of-concept transcriptionally regulated gene-trait associations, including UGT1A1 (encoding bilirubin uridine diphosphate glucuronosyltransferase enzyme) and total bilirubin levels (p = 3.59×10-12), and CETP (cholesteryl ester transfer protein) with high-density lipoprotein cholesterol (p = 4.49×10-12). We also identified several novel genes associated with metabolic and virologic traits, as well as pleiotropic genes that linked plasma viral load, absolute basophil count, and/or triglyceride levels. By highlighting the advantages of different TWAS methods, our simulation study promotes a tissue specificity-aware TWAS analytic framework that revealed novel aspects of HIV-related traits.
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http://dx.doi.org/10.1371/journal.pgen.1009464DOI Listing
April 2021

Genetic Associations with Weight Gain among South Africans who Initiated Dolutegravir- and Tenofovir-containing Regimens.

J Acquir Immune Defic Syndr 2021 Feb 17. Epub 2021 Feb 17.

Division of Clinical Pharmacology, Department of Medicine, University of Cape Town, Cape Town, South Africa Wellcome Centre for Infectious Diseases Research in Africa, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA Ezintsha, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA Department of Internal Medicine, Meharry Medical College, Nashville, Tennessee, USA Joint senior authors.

Background: Excessive weight gain affects some HIV-positive individuals prescribed dolutegravir- containing regimens. Mechanisms underlying such weight gain are unknown.

Setting: Data and DNA from antiretroviral therapy-naïve participants who were randomized to initiate dolutegravir with emtricitabine plus either tenofovir alafenamide (TAF) or tenofovir disoproxil fumarate (TDF) in the ADVANCE study (NCT03122262) were used to characterize associations between human genetic polymorphisms and magnitude of weight gain.

Methods: Associations with percent weight gain from baseline to week 48 were assessed using multivariable linear regression models. Primary analyses a priori considered 59 polymorphisms and 10 genes of potential relevance to dolutegravir, TAF or TDF pharmacokinetics. We also explored genome-wide associations.

Results: Among the 314 (92%) of 340 dolutegravir recipients who were successfully genotyped, 160 (47%) and 154 (45%) were randomized to TAF/emtricitabine and TDF/emtricitabine, respectively. In target gene analyses, the lowest P-values for the dolutegravir and tenofovir groups were ABCG2 rs4148149 (P = 7.0x10-4) and ABCC10 rs67861980 (P = 1.0x10-2), respectively, which were not significant after correction for multiple testing. In genome-wide analyses the lowest P-values for dolutegravir, was rs7590091 in TMEM163 (P = 3.7x10-8), rs17137701 in LOC105379130 (P = 6.4x10- 8) for TAF, and rs76771105 in LOC105371716 (P = 9.7x10-8) for TDF.

Conclusion: Among South African participants in a randomized clinical trial of dolutegravir plus either TAF/emtricitabine or TDF/emtricitabine, we identified several potential genetic associations with weight gain. Only TMEM163 rs7590091 withstood correction for multiple testing. These associations warrant replication in other cohorts.
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http://dx.doi.org/10.1097/QAI.0000000000002661DOI Listing
February 2021

Exome-wide evaluation of rare coding variants using electronic health records identifies new gene-phenotype associations.

Nat Med 2021 01 11;27(1):66-72. Epub 2021 Jan 11.

Division of Cardiothoracic Surgery, Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.

The clinical impact of rare loss-of-function variants has yet to be determined for most genes. Integration of DNA sequencing data with electronic health records (EHRs) could enhance our understanding of the contribution of rare genetic variation to human disease. By leveraging 10,900 whole-exome sequences linked to EHR data in the Penn Medicine Biobank, we addressed the association of the cumulative effects of rare predicted loss-of-function variants for each individual gene on human disease on an exome-wide scale, as assessed using a set of diverse EHR phenotypes. After discovering 97 genes with exome-by-phenome-wide significant phenotype associations (P < 10), we replicated 26 of these in the Penn Medicine Biobank, as well as in three other medical biobanks and the population-based UK Biobank. Of these 26 genes, five had associations that have been previously reported and represented positive controls, whereas 21 had phenotype associations not previously reported, among which were genes implicated in glaucoma, aortic ectasia, diabetes mellitus, muscular dystrophy and hearing loss. These findings show the value of aggregating rare predicted loss-of-function variants into 'gene burdens' for identifying new gene-disease associations using EHR phenotypes in a medical biobank. We suggest that application of this approach to even larger numbers of individuals will provide the statistical power required to uncover unexplored relationships between rare genetic variation and disease phenotypes.
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http://dx.doi.org/10.1038/s41591-020-1133-8DOI Listing
January 2021

Genetics of height and risk of atrial fibrillation: A Mendelian randomization study.

PLoS Med 2020 10 8;17(10):e1003288. Epub 2020 Oct 8.

Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, United States of America.

Background: Observational studies have identified height as a strong risk factor for atrial fibrillation, but this finding may be limited by residual confounding. We aimed to examine genetic variation in height within the Mendelian randomization (MR) framework to determine whether height has a causal effect on risk of atrial fibrillation.

Methods And Findings: In summary-level analyses, MR was performed using summary statistics from genome-wide association studies of height (GIANT/UK Biobank; 693,529 individuals) and atrial fibrillation (AFGen; 65,446 cases and 522,744 controls), finding that each 1-SD increase in genetically predicted height increased the odds of atrial fibrillation (odds ratio [OR] 1.34; 95% CI 1.29 to 1.40; p = 5 × 10-42). This result remained consistent in sensitivity analyses with MR methods that make different assumptions about the presence of pleiotropy, and when accounting for the effects of traditional cardiovascular risk factors on atrial fibrillation. Individual-level phenome-wide association studies of height and a height genetic risk score were performed among 6,567 European-ancestry participants of the Penn Medicine Biobank (median age at enrollment 63 years, interquartile range 55-72; 38% female; recruitment 2008-2015), confirming prior observational associations between height and atrial fibrillation. Individual-level MR confirmed that each 1-SD increase in height increased the odds of atrial fibrillation, including adjustment for clinical and echocardiographic confounders (OR 1.89; 95% CI 1.50 to 2.40; p = 0.007). The main limitations of this study include potential bias from pleiotropic effects of genetic variants, and lack of generalizability of individual-level findings to non-European populations.

Conclusions: In this study, we observed evidence that height is likely a positive causal risk factor for atrial fibrillation. Further study is needed to determine whether risk prediction tools including height or anthropometric risk factors can be used to improve screening and primary prevention of atrial fibrillation, and whether biological pathways involved in height may offer new targets for treatment of atrial fibrillation.
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http://dx.doi.org/10.1371/journal.pmed.1003288DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544133PMC
October 2020

Efavirenz Pharmacogenetics and Weight Gain following Switch to Integrase Inhibitor-containing Regimens.

Clin Infect Dis 2020 Aug 23. Epub 2020 Aug 23.

Vanderbilt University Medical Center, Nashville, TN, USA, USA.

Background: Unwanted weight gain affects some people living with HIV who are prescribed integrase strand transfer inhibitors (INSTI). Mechanisms and risk factors are incompletely understood.

Methods: We utilized two cohorts to study pharmacogenetics of weight gain following switch from efavirenz- to INSTI-based regimens. In an observational cohort, we studied weight gain at 48 weeks following switch from efavirenz- to INSTI-based regimens among patients who had been virologically suppressed for at least 2 years at a clinic in the United States. Associations were characterized with CYP2B6 and UGT1A1 genotypes that affect efavirenz and INSTI metabolism, respectively. In a clinical trials cohort, we studied weight gain at 48 weeks among treatment-naïve participants who were randomized to receive efavirenz-containing regimens in AIDS Clinical Trials Group studies A5095, A5142 and A5202 and did not receive INSTIs.

Results: In the observational cohort (N=61), CYP2B6 slow metabolizers had greater weight gain after switch (p=0.01). This was seen following switch to elvitegravir or raltegravir, but not dolutegravir. UGT1A1 genotype was not associated with weight gain. In the clinical trials cohort (N=462), CYP2B6 slow metabolizers had lesser weight gain at week 48 among participants receiving efavirenz with tenofovir disoproxil fumarate (p=0.001) but not those receiving efavirenz with abacavir (p=0.65). Findings were consistent when stratified by race/ethnicity and by sex.

Conclusions: Among patients who switched from efavirenz- to INSTI-based therapy, CYP2B6 genotype was associated with weight gain, possibly reflecting withdrawal of the inhibitory effect of higher efavirenz concentrations on weight gain. The difference by concomitant nucleoside analogue is unexplained.
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http://dx.doi.org/10.1093/cid/ciaa1219DOI Listing
August 2020

Genomewide Association Study of Platelet Reactivity and Cardiovascular Response in Patients Treated With Clopidogrel: A Study by the International Clopidogrel Pharmacogenomics Consortium.

Clin Pharmacol Ther 2020 11 9;108(5):1067-1077. Epub 2020 Jul 9.

Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA.

Antiplatelet response to clopidogrel shows wide variation, and poor response is correlated with adverse clinical outcomes. CYP2C19 loss-of-function alleles play an important role in this response, but account for only a small proportion of variability in response to clopidogrel. An aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify other genetic determinants of clopidogrel pharmacodynamics and clinical response. A genomewide association study (GWAS) was performed using DNA from 2,750 European ancestry individuals, using adenosine diphosphate-induced platelet reactivity and major cardiovascular and cerebrovascular events as outcome parameters. GWAS for platelet reactivity revealed a strong signal for CYP2C19*2 (P value = 1.67e-33). After correction for CYP2C19*2 no other single-nucleotide polymorphism reached genomewide significance. GWAS for a combined clinical end point of cardiovascular death, myocardial infarction, or stroke (5.0% event rate), or a combined end point of cardiovascular death or myocardial infarction (4.7% event rate) showed no significant results, although in coronary artery disease, percutaneous coronary intervention, and acute coronary syndrome subgroups, mutations in SCOS5P1, CDC42BPA, and CTRAC1 showed genomewide significance (lowest P values: 1.07e-09, 4.53e-08, and 2.60e-10, respectively). CYP2C19*2 is the strongest genetic determinant of on-clopidogrel platelet reactivity. We identified three novel associations in clinical outcome subgroups, suggestive for each of these outcomes.
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http://dx.doi.org/10.1002/cpt.1911DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7689744PMC
November 2020

A phenome-wide association study (PheWAS) in the Population Architecture using Genomics and Epidemiology (PAGE) study reveals potential pleiotropy in African Americans.

PLoS One 2019 31;14(12):e0226771. Epub 2019 Dec 31.

Cleveland Institute for Computational Biology, Cleveland, Ohio, United States of America.

We performed a hypothesis-generating phenome-wide association study (PheWAS) to identify and characterize cross-phenotype associations, where one SNP is associated with two or more phenotypes, between thousands of genetic variants assayed on the Metabochip and hundreds of phenotypes in 5,897 African Americans as part of the Population Architecture using Genomics and Epidemiology (PAGE) I study. The PAGE I study was a National Human Genome Research Institute-funded collaboration of four study sites accessing diverse epidemiologic studies genotyped on the Metabochip, a custom genotyping chip that has dense coverage of regions in the genome previously associated with cardio-metabolic traits and outcomes in mostly European-descent populations. Here we focus on identifying novel phenome-genome relationships, where SNPs are associated with more than one phenotype. To do this, we performed a PheWAS, testing each SNP on the Metabochip for an association with up to 273 phenotypes in the participating PAGE I study sites. We identified 133 putative pleiotropic variants, defined as SNPs associated at an empirically derived p-value threshold of p<0.01 in two or more PAGE study sites for two or more phenotype classes. We further annotated these PheWAS-identified variants using publicly available functional data and local genetic ancestry. Amongst our novel findings is SPARC rs4958487, associated with increased glucose levels and hypertension. SPARC has been implicated in the pathogenesis of diabetes and is also known to have a potential role in fibrosis, a common consequence of multiple conditions including hypertension. The SPARC example and others highlight the potential that PheWAS approaches have in improving our understanding of complex disease architecture by identifying novel relationships between genetic variants and an array of common human phenotypes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0226771PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6938343PMC
April 2020

Mitochondrial DNA Haplogroups and Frailty in Adults Living with HIV.

AIDS Res Hum Retroviruses 2020 03 14;36(3):214-219. Epub 2020 Jan 14.

Division of Infectious Diseases, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee.

Mitochondrial DNA (mtDNA) haplogroup has been associated with disease risk and longevity. Among persons with HIV (PWH), mtDNA haplogroup has been associated with AIDS progression, neuropathy, cognitive impairment, and gait speed decline. We sought to determine whether haplogroup is associated with frailty and its components among older PWH. A cross-sectional analysis was performed of AIDS Clinical Trials Group A5322 (HAILO) participants with available genome-wide genotype and frailty assessments. Multivariable logistic regression models adjusted for age, gender, education, smoking, hepatitis C, and prior use of didanosine/stavudine. Among 634 participants, 81% were male, 49% non-Hispanic white, 31% non-Hispanic black, and 20% Hispanic. Mean age was 51.0 (standard deviation 7.5) years and median nadir CD4 count was 212 (interquartile range 72, 324) cells/μL; 6% were frail, 7% had slow gait, and 21% weak grip. H haplogroup participants were more likely to be frail/prefrail ( = .064), have slow gait ( = .09), or weak grip ( = .017) compared with non-H haplogroup participants (not all comparisons reached statistical significance). In adjusted analyses, PWH with haplogroup H had a greater odds of being frail versus nonfrail [odds ratio (OR) 4.0 (95% confidence interval 1.0-15.4)] and having weak grip [OR 2.1 (1.1, 4.1)], but not slow gait [OR 1.6 (0.5, 5.0)] compared with non-H haplogroup. Among black and Hispanic participants, haplogroup was not significantly associated with frailty, grip, or gait. Among antiretroviral therapy (ART)-treated PWH, mtDNA haplogroup H was independently associated with weak grip and frailty. This association could represent a mechanism of weakness and frailty in the setting of HIV and ART.
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http://dx.doi.org/10.1089/AID.2019.0233DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7133433PMC
March 2020

Cross-Cancer Pleiotropic Associations with Lung Cancer Risk in African Americans.

Cancer Epidemiol Biomarkers Prev 2019 04 20;28(4):715-723. Epub 2019 Mar 20.

Department of Thoracic Surgery, Vanderbilt University Medical Center, Nashville, Tennessee.

Background: Identifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European-descent individuals. Yet population-specific genetic variation can occur, and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.

Methods: We conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.

Results: We identified three novel genomic regions significantly associated (FDR-corrected <0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with reduced risk [OR = 0.43; 95% confidence interval (CI), 0.73-0.89], and functional annotation using GTEx showed rs7186207 modifies gene expression. The minor allele at rs336958 on 5q14.3 was associated with increased lung cancer risk (OR = 1.47; 95% CI, 1.22-1.78), whereas the minor allele at rs11658063 on 17q12 was associated with reduced risk (OR = 0.80; 95% CI, 0.72-0.90).

Conclusions: We identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain , and genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.

Impact: Our findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.
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http://dx.doi.org/10.1158/1055-9965.EPI-18-0935DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6449205PMC
April 2019

Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies.

Pac Symp Biocomput 2019 ;24:296-307

Genomics and Computational Biology Program, University of Pennsylvania Philadelphia, PA 19104, USA,

Transcriptome-wide association studies (TWAS) have recently gained great attention due to their ability to prioritize complex trait-associated genes and promote potential therapeutics development for complex human diseases. TWAS integrates genotypic data with expression quantitative trait loci (eQTLs) to predict genetically regulated gene expression components and associates predictions with a trait of interest. As such, TWAS can prioritize genes whose differential expressions contribute to the trait of interest and provide mechanistic explanation of complex trait(s). Tissue-specific eQTL information grants TWAS the ability to perform association analysis on tissues whose gene expression profiles are otherwise hard to obtain, such as liver and heart. However, as eQTLs are tissue context-dependent, whether and how the tissue-specificity of eQTLs influences TWAS gene prioritization has not been fully investigated. In this study, we addressed this question by adopting two distinct TWAS methods, PrediXcan and UTMOST, which assume single tissue and integrative tissue effects of eQTLs, respectively. Thirty-eight baseline laboratory traits in 4,360 antiretroviral treatment-naïve individuals from the AIDS Clinical Trials Group (ACTG) studies comprised the input dataset for TWAS. We performed TWAS in a tissue-specific manner and obtained a total of 430 significant gene-trait associations (q-value < 0.05) across multiple tissues. Single tissue-based analysis by PrediXcan contributed 116 of the 430 associations including 64 unique gene-trait pairs in 28 tissues. Integrative tissue-based analysis by UTMOST found the other 314 significant associations that include 50 unique gene-trait pairs across all 44 tissues. Both analyses were able to replicate some associations identified in past variant-based genome-wide association studies (GWAS), such as high-density lipoprotein (HDL) and CETP (PrediXcan, q-value = 3.2e-16). Both analyses also identified novel associations. Moreover, single tissue-based and integrative tissuebased analysis shared 11 of 103 unique gene-trait pairs, for example, PSRC1-low-density lipoprotein (PrediXcan's lowest q-value = 8.5e-06; UTMOST's lowest q-value = 1.8e-05). This study suggests that single tissue-based analysis may have performed better at discovering gene-trait associations when combining results from all tissues. Integrative tissue-based analysis was better at prioritizing genes in multiple tissues and in trait-related tissue. Additional exploration is needed to confirm this conclusion. Finally, although single tissue-based and integrative tissue-based analysis shared significant novel discoveries, tissue context-dependency of eQTLs impacted TWAS gene prioritization. This study provides preliminary data to support continued work on tissue contextdependency of eQTL studies and TWAS.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6417797PMC
January 2020

PR interval genome-wide association meta-analysis identifies 50 loci associated with atrial and atrioventricular electrical activity.

Nat Commun 2018 07 25;9(1):2904. Epub 2018 Jul 25.

Department of Biomedical Engineering, Johns Hopkins University, Baltimore, 21218, MD, USA.

Electrocardiographic PR interval measures atrio-ventricular depolarization and conduction, and abnormal PR interval is a risk factor for atrial fibrillation and heart block. Our genome-wide association study of over 92,000 European-descent individuals identifies 44 PR interval loci (34 novel). Examination of these loci reveals known and previously not-yet-reported biological processes involved in cardiac atrial electrical activity. Genes in these loci are over-represented in cardiac disease processes including heart block and atrial fibrillation. Variants in over half of the 44 loci were associated with atrial or blood transcript expression levels, or were in high linkage disequilibrium with missense variants. Six additional loci were identified either by meta-analysis of ~105,000 African and European-descent individuals and/or by pleiotropic analyses combining PR interval with heart rate, QRS interval, and atrial fibrillation. These findings implicate developmental pathways, and identify transcription factors, ion-channel genes, and cell-junction/cell-signaling proteins in atrio-ventricular conduction, identifying potential targets for drug development.
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http://dx.doi.org/10.1038/s41467-018-04766-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6060178PMC
July 2018

Brain neurotransmitter transporter/receptor genomics and efavirenz central nervous system adverse events.

Pharmacogenet Genomics 2018 07;28(7):179-187

Department of Genetics.

Objective: We characterized associations between central nervous system (CNS) adverse events and brain neurotransmitter transporter/receptor genomics among participants randomized to efavirenz-containing regimens in AIDS Clinical Trials Group studies in the USA.

Participants And Methods: Four clinical trials randomly assigned treatment-naive participants to efavirenz-containing regimens. Genome-wide genotype and PrediXcan were used to infer gene expression levels in tissues including 10 brain regions. Multivariable regression models stratified by race/ethnicity were adjusted for CYP2B6/CYP2A6 genotypes that predict plasma efavirenz exposure, age, and sex. Combined analyses also adjusted for genetic ancestry.

Results: Analyses included 167 cases with grade 2 or greater efavirenz-consistent CNS adverse events within 48 weeks of study entry, and 653 efavirenz-tolerant controls. CYP2B6/CYP2A6 genotype level was independently associated with CNS adverse events (odds ratio: 1.07; P=0.044). Predicted expression of six genes postulated to mediate efavirenz CNS side effects (SLC6A2, SLC6A3, PGR, HTR2A, HTR2B, HTR6) were not associated with CNS adverse events after correcting for multiple testing, the lowest P value being for PGR in hippocampus (P=0.012), nor were polymorphisms in these genes or AR and HTR2C, the lowest P value being for rs12393326 in HTR2C (P=6.7×10(-4)). As a positive control, baseline plasma bilirubin concentration was associated with predicted liver UGT1A1 expression level (P=1.9×10(-27)).

Conclusion: Efavirenz-related CNS adverse events were not associated with predicted neurotransmitter transporter/receptor gene expression levels in brain or with polymorphisms in these genes. Variable susceptibility to efavirenz-related CNS adverse events may not be explained by brain neurotransmitter transporter/receptor genomics.
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http://dx.doi.org/10.1097/FPC.0000000000000341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6010221PMC
July 2018

A simulation study investigating power estimates in phenome-wide association studies.

BMC Bioinformatics 2018 04 4;19(1):120. Epub 2018 Apr 4.

Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA.

Background: Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.

Results: We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.

Conclusions: This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.
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http://dx.doi.org/10.1186/s12859-018-2135-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5885318PMC
April 2018

Evaluation of PrediXcan for prioritizing GWAS associations and predicting gene expression.

Pac Symp Biocomput 2018 ;23:448-459

The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States.

Genome-wide association studies (GWAS) have been successful in facilitating the understanding of genetic architecture behind human diseases, but this approach faces many challenges. To identify disease-related loci with modest to weak effect size, GWAS requires very large sample sizes, which can be computational burdensome. In addition, the interpretation of discovered associations remains difficult. PrediXcan was developed to help address these issues. With built in SNP-expression models, PrediXcan is able to predict the expression of genes that are regulated by putative expression quantitative trait loci (eQTLs), and these predicted expression levels can then be used to perform gene-based association studies. This approach reduces the multiple testing burden from millions of variants down to several thousand genes. But most importantly, the identified associations can reveal the genes that are under regulation of eQTLs and consequently involved in disease pathogenesis. In this study, two of the most practical functions of PrediXcan were tested: 1) predicting gene expression, and 2) prioritizing GWAS results. We tested the prediction accuracy of PrediXcan by comparing the predicted and observed gene expression levels, and also looked into some potential influential factors and a filter criterion with the aim of improving PrediXcan performance. As for GWAS prioritization, predicted gene expression levels were used to obtain gene-trait associations, and background regions of significant associations were examined to decrease the likelihood of false positives. Our results showed that 1) PrediXcan predicted gene expression levels accurately for some but not all genes; 2) including more putative eQTLs into prediction did not improve the prediction accuracy; and 3) integrating predicted gene expression levels from the two PrediXcan whole blood models did not eliminate false positives. Still, PrediXcan was able to prioritize GWAS associations that were below the genome-wide significance threshold in GWAS, while retaining GWAS significant results. This study suggests several ways to consider PrediXcan's performance that will be of value to eQTL and complex human disease research.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5749400PMC
August 2018

Genetic Susceptibility to Postdiarrheal Hemolytic-Uremic Syndrome After Shiga Toxin-Producing Escherichia coli Infection: A Centers for Disease Control and Prevention FoodNet Study.

J Infect Dis 2018 03;217(6):1000-1010

Tennessee Department of Health, Nashville.

Background: Postdiarrheal hemolytic-uremic syndrome (D+HUS) following Shiga toxin-producing Escherichia coli (STEC) infection is a serious condition lacking specific treatment. Host immune dysregulation and genetic susceptibility to complement hyperactivation are implicated in non-STEC-related HUS. However, genetic susceptibility to D+HUS remains largely uncharacterized.

Methods: Patients with culture-confirmed STEC diarrhea, identified through the Centers for Disease Control and Prevention FoodNet surveillance system (2007-2012), were serotyped and classified by laboratory and/or clinical criteria as having suspected, probable, or confirmed D+HUS or as controls and underwent genotyping at 200 loci linked to nondiarrheal HUS or similar pathologies. Genetic associations with D+HUS were explored by multivariable regression, with adjustment for known risk factors.

Results: Of 641 enrollees with STEC O157:H7, 80 had suspected D+HUS (41 with probable and 32 with confirmed D+HUS). Twelve genes related to cytokine signaling, complement pathways, platelet function, pathogen recognition, iron transport, and endothelial function were associated with D+HUS in multivariable-adjusted analyses (P ≤ .05). Of 12 significant single-nucleotide polymorphisms (SNPs), 5 were associated with all levels of D+HUS (intergenic SNP rs10874639, TFRC rs3804141, EDN1 rs5370, GP1BA rs121908064, and B2M rs16966334), and 7 SNPs (6 non-complement related) were associated with confirmed D+HUS (all P < .05).

Conclusions: Polymorphisms in many non-complement-related genes may contribute to D+HUS susceptibility. These results require replication, but they suggest novel therapeutic targets in patients with D+HUS.
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http://dx.doi.org/10.1093/infdis/jix633DOI Listing
March 2018

Genome-wide study of resistant hypertension identified from electronic health records.

PLoS One 2017 21;12(2):e0171745. Epub 2017 Feb 21.

Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States of America.

Resistant hypertension is defined as high blood pressure that remains above treatment goals in spite of the concurrent use of three antihypertensive agents from different classes. Despite the important health consequences of resistant hypertension, few studies of resistant hypertension have been conducted. To perform a genome-wide association study for resistant hypertension, we defined and identified cases of resistant hypertension and hypertensives with treated, controlled hypertension among >47,500 adults residing in the US linked to electronic health records (EHRs) and genotyped as part of the electronic MEdical Records & GEnomics (eMERGE) Network. Electronic selection logic using billing codes, laboratory values, text queries, and medication records was used to identify resistant hypertension cases and controls at each site, and a total of 3,006 cases of resistant hypertension and 876 controlled hypertensives were identified among eMERGE Phase I and II sites. After imputation and quality control, a total of 2,530,150 SNPs were tested for an association among 2,830 multi-ethnic cases of resistant hypertension and 876 controlled hypertensives. No test of association was genome-wide significant in the full dataset or in the dataset limited to European American cases (n = 1,719) and controls (n = 708). The most significant finding was CLNK rs13144136 at p = 1.00x10-6 (odds ratio = 0.68; 95% CI = 0.58-0.80) in the full dataset with similar results in the European American only dataset. We also examined whether SNPs known to influence blood pressure or hypertension also influenced resistant hypertension. None was significant after correction for multiple testing. These data highlight both the difficulties and the potential utility of EHR-linked genomic data to study clinically-relevant traits such as resistant hypertension.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0171745PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5319785PMC
August 2017

Multiphenotype association study of patients randomized to initiate antiretroviral regimens in AIDS Clinical Trials Group protocol A5202.

Pharmacogenet Genomics 2017 03;27(3):101-111

aThe Center for Systems Genomics, The Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park bBiomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania cLos Angeles Biomedical Research Institute at Harbor, UCLA Medical Center, Torrance, California dUniversity of Rochester Medical Center, Rochester eDepartment of Pharmacy Practice, Center for Integrated Global Biomedical Sciences, University at Buffalo, SUNY, Buffalo, New York fVanderbilt University School of Medicine gMeharry Medical College, Nashville, Tennessee, USA.

Background: High-throughput approaches are increasingly being used to identify genetic associations across multiple phenotypes simultaneously. Here, we describe a pilot analysis that considered multiple on-treatment laboratory phenotypes from antiretroviral therapy-naive patients who were randomized to initiate antiretroviral regimens in a prospective clinical trial, AIDS Clinical Trials Group protocol A5202.

Participants And Methods: From among 5 9545 294 polymorphisms imputed genome-wide, we analyzed 2544, including 2124 annotated in the PharmGKB, and 420 previously associated with traits in the GWAS Catalog. We derived 774 phenotypes on the basis of context from six variables: plasma atazanavir (ATV) pharmacokinetics, plasma efavirenz (EFV) pharmacokinetics, change in the CD4+ T-cell count, HIV-1 RNA suppression, fasting low-density lipoprotein-cholesterol, and fasting triglycerides. Permutation testing assessed the likelihood of associations being by chance alone. Pleiotropy was assessed for polymorphisms with the lowest P-values.

Results: This analysis included 1181 patients. At P less than 1.5×10, most associations were not by chance alone. Polymorphisms with the lowest P-values for EFV pharmacokinetics (CYPB26 rs3745274), low-density lipoprotein -cholesterol (APOE rs7412), and triglyceride (APOA5 rs651821) phenotypes had been associated previously with those traits in previous studies. The association between triglycerides and rs651821 was present with ATV-containing regimens, but not with EFV-containing regimens. Polymorphisms with the lowest P-values for ATV pharmacokinetics, CD4 T-cell count, and HIV-1 RNA phenotypes had not been reported previously to be associated with that trait.

Conclusion: Using data from a prospective HIV clinical trial, we identified expected genetic associations, potentially novel associations, and at least one context-dependent association. This study supports high-throughput strategies that simultaneously explore multiple phenotypes from clinical trials' datasets for genetic associations.
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http://dx.doi.org/10.1097/FPC.0000000000000263DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5285297PMC
March 2017

Epistatic Gene-Based Interaction Analyses for Glaucoma in eMERGE and NEIGHBOR Consortium.

PLoS Genet 2016 09 13;12(9):e1006186. Epub 2016 Sep 13.

Department of Biomedical and Translational Informatics, Geisinger Health System, Danville, Pennsylvania, United States of America.

Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.
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http://dx.doi.org/10.1371/journal.pgen.1006186DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5021356PMC
September 2016

eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants.

BMC Med Genomics 2016 08 12;9 Suppl 1:32. Epub 2016 Aug 12.

Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Tygerberg, 7505, South Africa.

Background: We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy.

Results: In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272.1 "Disorder of Lipoid metabolism" (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 "Acquired Hypothyroidism" (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 "Complications peculiar to certain specified procedures" (pdiscovery = 8.65x103, preplicating = 4.16x10-3).

Conclusion: In conclusion, this PheWAS revealed novel associations of stop-gained variants with interesting phenotypes (ICD-9 codes) along with pleiotropic effects.
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http://dx.doi.org/10.1186/s12920-016-0191-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4989894PMC
August 2016

Phenome-Wide Association Study to Explore Relationships between Immune System Related Genetic Loci and Complex Traits and Diseases.

PLoS One 2016 10;11(8):e0160573. Epub 2016 Aug 10.

Biomedical and Translational Informatics Program, Geisinger Health System, Danville, Pennsylvania, United States of America.

We performed a Phenome-Wide Association Study (PheWAS) to identify interrelationships between the immune system genetic architecture and a wide array of phenotypes from two de-identified electronic health record (EHR) biorepositories. We selected variants within genes encoding critical factors in the immune system and variants with known associations with autoimmunity. To define case/control status for EHR diagnoses, we used International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes from 3,024 Geisinger Clinic MyCode® subjects (470 diagnoses) and 2,899 Vanderbilt University Medical Center BioVU biorepository subjects (380 diagnoses). A pooled-analysis was also carried out for the replicating results of the two data sets. We identified new associations with potential biological relevance including SNPs in tumor necrosis factor (TNF) and ankyrin-related genes associated with acute and chronic sinusitis and acute respiratory tract infection. The two most significant associations identified were for the C6orf10 SNP rs6910071 and "rheumatoid arthritis" (ICD-9 code category 714) (pMETAL = 2.58 x 10-9) and the ATN1 SNP rs2239167 and "diabetes mellitus, type 2" (ICD-9 code category 250) (pMETAL = 6.39 x 10-9). This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0160573PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4980020PMC
August 2017

Identification of genetic interaction networks via an evolutionary algorithm evolved Bayesian network.

BioData Min 2016 10;9:18. Epub 2016 May 10.

Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA ; Biomedical & Translational Informatics, Geisinger Health System, Danville, Pennsylvania USA.

Background: The future of medicine is moving towards the phase of precision medicine, with the goal to prevent and treat diseases by taking inter-individual variability into account. A large part of the variability lies in our genetic makeup. With the fast paced improvement of high-throughput methods for genome sequencing, a tremendous amount of genetics data have already been generated. The next hurdle for precision medicine is to have sufficient computational tools for analyzing large sets of data. Genome-Wide Association Studies (GWAS) have been the primary method to assess the relationship between single nucleotide polymorphisms (SNPs) and disease traits. While GWAS is sufficient in finding individual SNPs with strong main effects, it does not capture potential interactions among multiple SNPs. In many traits, a large proportion of variation remain unexplained by using main effects alone, leaving the door open for exploring the role of genetic interactions. However, identifying genetic interactions in large-scale genomics data poses a challenge even for modern computing.

Results: For this study, we present a new algorithm, Grammatical Evolution Bayesian Network (GEBN) that utilizes Bayesian Networks to identify interactions in the data, and at the same time, uses an evolutionary algorithm to reduce the computational cost associated with network optimization. GEBN excelled in simulation studies where the data contained main effects and interaction effects. We also applied GEBN to a Type 2 diabetes (T2D) dataset obtained from the Marshfield Personalized Medicine Research Project (PMRP). We were able to identify genetic interactions for T2D cases and controls and use information from those interactions to classify T2D samples. We obtained an average testing area under the curve (AUC) of 86.8 %. We also identified several interacting genes such as INADL and LPP that are known to be associated with T2D.

Conclusions: Developing the computational tools to explore genetic associations beyond main effects remains a critically important challenge in human genetics. Methods, such as GEBN, demonstrate the utility of considering genetic interactions, as they likely explain some of the missing heritability.
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http://dx.doi.org/10.1186/s13040-016-0094-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4862166PMC
May 2016

BIOFILTER AS A FUNCTIONAL ANNOTATION PIPELINE FOR COMMON AND RARE COPY NUMBER BURDEN.

Pac Symp Biocomput 2016 ;21:357-68

Center for Systems Genomics, Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, Pennsylvania, USA.

Recent studies on copy number variation (CNV) have suggested that an increasing burden of CNVs is associated with susceptibility or resistance to disease. A large number of genes or genomic loci contribute to complex diseases such as autism. Thus, total genomic copy number burden, as an accumulation of copy number change, is a meaningful measure of genomic instability to identify the association between global genetic effects and phenotypes of interest. However, no systematic annotation pipeline has been developed to interpret biological meaning based on the accumulation of copy number change across the genome associated with a phenotype of interest. In this study, we develop a comprehensive and systematic pipeline for annotating copy number variants into genes/genomic regions and subsequently pathways and other gene groups using Biofilter - a bioinformatics tool that aggregates over a dozen publicly available databases of prior biological knowledge. Next we conduct enrichment tests of biologically defined groupings of CNVs including genes, pathways, Gene Ontology, or protein families. We applied the proposed pipeline to a CNV dataset from the Marshfield Clinic Personalized Medicine Research Project (PMRP) in a quantitative trait phenotype derived from the electronic health record - total cholesterol. We identified several significant pathways such as toll-like receptor signaling pathway and hepatitis C pathway, gene ontologies (GOs) of nucleoside triphosphatase activity (NTPase) and response to virus, and protein families such as cell morphogenesis that are associated with the total cholesterol phenotype based on CNV profiles (permutation p-value < 0.01). Based on the copy number burden analysis, it follows that the more and larger the copy number changes, the more likely that one or more target genes that influence disease risk and phenotypic severity will be affected. Thus, our study suggests the proposed enrichment pipeline could improve the interpretability of copy number burden analysis where hundreds of loci or genes contribute toward disease susceptibility via biological knowledge groups such as pathways. This CNV annotation pipeline with Biofilter can be used for CNV data from any genotyping or sequencing platform and to explore CNV enrichment for any traits or phenotypes. Biofilter continues to be a powerful bioinformatics tool for annotating, filtering, and constructing biologically informed models for association analysis - now including copy number variants.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722964PMC
October 2016

Genome-Wide Association Study of Serum Creatinine Levels during Vancomycin Therapy.

PLoS One 2015 1;10(6):e0127791. Epub 2015 Jun 1.

Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America; Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, United States of America.

Vancomycin, a commonly used antibiotic, can be nephrotoxic. Known risk factors such as age, creatinine clearance, vancomycin dose / dosing interval, and concurrent nephrotoxic medications fail to accurately predict nephrotoxicity. To identify potential genomic risk factors, we performed a genome-wide association study (GWAS) of serum creatinine levels while on vancomycin in 489 European American individuals and validated findings in three independent cohorts totaling 439 European American individuals. In primary analyses, the chromosome 6q22.31 locus was associated with increased serum creatinine levels while on vancomycin therapy (most significant variant rs2789047, risk allele A, β = -0.06, p = 1.1 x 10(-7)). SNPs in this region had consistent directions of effect in the validation cohorts, with a meta-p of 1.1 x 10(-7). Variation in this region on chromosome 6, which includes the genes TBC1D32/C6orf170 and GJA1 (encoding connexin43), may modulate risk of vancomycin-induced kidney injury.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0127791PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4452656PMC
March 2016

Imputation and quality control steps for combining multiple genome-wide datasets.

Front Genet 2014 11;5:370. Epub 2014 Dec 11.

Department of Biochemistry and Molecular Biology, Center for Systems Genomics, The Pennsylvania State University Pennsylvania, PA, USA.

The electronic MEdical Records and GEnomics (eMERGE) network brings together DNA biobanks linked to electronic health records (EHRs) from multiple institutions. Approximately 51,000 DNA samples from distinct individuals have been genotyped using genome-wide SNP arrays across the nine sites of the network. The eMERGE Coordinating Center and the Genomics Workgroup developed a pipeline to impute and merge genomic data across the different SNP arrays to maximize sample size and power to detect associations with a variety of clinical endpoints. The 1000 Genomes cosmopolitan reference panel was used for imputation. Imputation results were evaluated using the following metrics: accuracy of imputation, allelic R (2) (estimated correlation between the imputed and true genotypes), and the relationship between allelic R (2) and minor allele frequency. Computation time and memory resources required by two different software packages (BEAGLE and IMPUTE2) were also evaluated. A number of challenges were encountered due to the complexity of using two different imputation software packages, multiple ancestral populations, and many different genotyping platforms. We present lessons learned and describe the pipeline implemented here to impute and merge genomic data sets. The eMERGE imputed dataset will serve as a valuable resource for discovery, leveraging the clinical data that can be mined from the EHR.
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http://dx.doi.org/10.3389/fgene.2014.00370DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4263197PMC
January 2015

Genetic variants associated with serum thyroid stimulating hormone (TSH) levels in European Americans and African Americans from the eMERGE Network.

PLoS One 2014 1;9(12):e111301. Epub 2014 Dec 1.

Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, United States of America; Center for Human Genetics Research, Vanderbilt University, Nashville, TN, United States of America.

Thyroid stimulating hormone (TSH) hormone levels are normally tightly regulated within an individual; thus, relatively small variations may indicate thyroid disease. Genome-wide association studies (GWAS) have identified variants in PDE8B and FOXE1 that are associated with TSH levels. However, prior studies lacked racial/ethnic diversity, limiting the generalization of these findings to individuals of non-European ethnicities. The Electronic Medical Records and Genomics (eMERGE) Network is a collaboration across institutions with biobanks linked to electronic medical records (EMRs). The eMERGE Network uses EMR-derived phenotypes to perform GWAS in diverse populations for a variety of phenotypes. In this report, we identified serum TSH levels from 4,501 European American and 351 African American euthyroid individuals in the eMERGE Network with existing GWAS data. Tests of association were performed using linear regression and adjusted for age, sex, body mass index (BMI), and principal components, assuming an additive genetic model. Our results replicate the known association of PDE8B with serum TSH levels in European Americans (rs2046045 p = 1.85×10-17, β = 0.09). FOXE1 variants, associated with hypothyroidism, were not genome-wide significant (rs10759944: p = 1.08×10-6, β = -0.05). No SNPs reached genome-wide significance in African Americans. However, multiple known associations with TSH levels in European ancestry were nominally significant in African Americans, including PDE8B (rs2046045 p = 0.03, β = -0.09), VEGFA (rs11755845 p = 0.01, β = -0.13), and NFIA (rs334699 p = 1.50×10-3, β = -0.17). We found little evidence that SNPs previously associated with other thyroid-related disorders were associated with serum TSH levels in this study. These results support the previously reported association between PDE8B and serum TSH levels in European Americans and emphasize the need for additional genetic studies in more diverse populations.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0111301PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4249871PMC
February 2016

Controlling for population structure and genotyping platform bias in the eMERGE multi-institutional biobank linked to electronic health records.

Front Genet 2014 4;5:352. Epub 2014 Nov 4.

Division of Biomedical Statistics and Informatics, Mayo Clinic Rochester, MN, USA.

Combining samples across multiple cohorts in large-scale scientific research programs is often required to achieve the necessary power for genome-wide association studies. Controlling for genomic ancestry through principal component analysis (PCA) to address the effect of population stratification is a common practice. In addition to local genomic variation, such as copy number variation and inversions, other factors directly related to combining multiple studies, such as platform and site recruitment bias, can drive the correlation patterns in PCA. In this report, we describe the combination and analysis of multi-ethnic cohort with biobanks linked to electronic health records for large-scale genomic association discovery analyses. First, we outline the observed site and platform bias, in addition to ancestry differences. Second, we outline a general protocol for selecting variants for input into the subject variance-covariance matrix, the conventional PCA approach. Finally, we introduce an alternative approach to PCA by deriving components from subject loadings calculated from a reference sample. This alternative approach of generating principal components controlled for site and platform bias, in addition to ancestry differences, has the advantage of fewer covariates and degrees of freedom.
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http://dx.doi.org/10.3389/fgene.2014.00352DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4220165PMC
November 2014

Phenome-wide association studies demonstrating pleiotropy of genetic variants within FTO with and without adjustment for body mass index.

Front Genet 2014 5;5:250. Epub 2014 Aug 5.

Division of Cardiovascular Diseases, Mayo Clinic Rochester, MN, USA.

Phenome-wide association studies (PheWAS) have demonstrated utility in validating genetic associations derived from traditional genetic studies as well as identifying novel genetic associations. Here we used an electronic health record (EHR)-based PheWAS to explore pleiotropy of genetic variants in the fat mass and obesity associated gene (FTO), some of which have been previously associated with obesity and type 2 diabetes (T2D). We used a population of 10,487 individuals of European ancestry with genome-wide genotyping from the Electronic Medical Records and Genomics (eMERGE) Network and another population of 13,711 individuals of European ancestry from the BioVU DNA biobank at Vanderbilt genotyped using Illumina HumanExome BeadChip. A meta-analysis of the two study populations replicated the well-described associations between FTO variants and obesity (odds ratio [OR] = 1.25, 95% Confidence Interval = 1.11-1.24, p = 2.10 × 10(-9)) and FTO variants and T2D (OR = 1.14, 95% CI = 1.08-1.21, p = 2.34 × 10(-6)). The meta-analysis also demonstrated that FTO variant rs8050136 was significantly associated with sleep apnea (OR = 1.14, 95% CI = 1.07-1.22, p = 3.33 × 10(-5)); however, the association was attenuated after adjustment for body mass index (BMI). Novel phenotype associations with obesity-associated FTO variants included fibrocystic breast disease (rs9941349, OR = 0.81, 95% CI = 0.74-0.91, p = 5.41 × 10(-5)) and trends toward associations with non-alcoholic liver disease and gram-positive bacterial infections. FTO variants not associated with obesity demonstrated other potential disease associations including non-inflammatory disorders of the cervix and chronic periodontitis. These results suggest that genetic variants in FTO may have pleiotropic associations, some of which are not mediated by obesity.
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http://dx.doi.org/10.3389/fgene.2014.00250DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4134007PMC
September 2014

Genetic association study of QT interval highlights role for calcium signaling pathways in myocardial repolarization.

Nat Genet 2014 Aug 22;46(8):826-36. Epub 2014 Jun 22.

Center for Biomedicine, European Academy Bozen/Bolzano (EURAC), Bolzano, Italy (affiliated institute of the University of Lübeck, Lübeck, Germany).

The QT interval, an electrocardiographic measure reflecting myocardial repolarization, is a heritable trait. QT prolongation is a risk factor for ventricular arrhythmias and sudden cardiac death (SCD) and could indicate the presence of the potentially lethal mendelian long-QT syndrome (LQTS). Using a genome-wide association and replication study in up to 100,000 individuals, we identified 35 common variant loci associated with QT interval that collectively explain ∼8-10% of QT-interval variation and highlight the importance of calcium regulation in myocardial repolarization. Rare variant analysis of 6 new QT interval-associated loci in 298 unrelated probands with LQTS identified coding variants not found in controls but of uncertain causality and therefore requiring validation. Several newly identified loci encode proteins that physically interact with other recognized repolarization proteins. Our integration of common variant association, expression and orthogonal protein-protein interaction screens provides new insights into cardiac electrophysiology and identifies new candidate genes for ventricular arrhythmias, LQTS and SCD.
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http://dx.doi.org/10.1038/ng.3014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4124521PMC
August 2014

KIAA1462, a coronary artery disease associated gene, is a candidate gene for late onset Alzheimer disease in APOE carriers.

PLoS One 2013 12;8(12):e82194. Epub 2013 Dec 12.

Center for Human Genetics Research and Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee, United States of America.

Alzheimer disease (AD) is a devastating neurodegenerative disease affecting more than five million Americans. In this study, we have used updated genetic linkage data from chromosome 10 in combination with expression data from serial analysis of gene expression to choose a new set of thirteen candidate genes for genetic analysis in late onset Alzheimer disease (LOAD). Results in this study identify the KIAA1462 locus as a candidate locus for LOAD in APOE4 carriers. Two genes exist at this locus, KIAA1462, a gene associated with coronary artery disease, and "rokimi", encoding an untranslated spliced RNA The genetic architecture at this locus suggests that the gene product important in this association is either "rokimi", or a different isoform of KIAA1462 than the isoform that is important in cardiovascular disease. Expression data suggests that isoform f of KIAA1462 is a more attractive candidate for association with LOAD in APOE4 carriers than "rokimi" which had no detectable expression in brain.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0082194PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3861372PMC
October 2014

Genetic variants associated with angiotensin-converting enzyme inhibitor-associated angioedema.

Pharmacogenet Genomics 2013 Sep;23(9):470-8

Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada.

Objective: The objective of this study was to identify genetic variants associated with angiotensin-converting enzyme (ACE) inhibitor-associated angioedema.

Participants And Methods: We carried out a genome-wide association study in 175 individuals with ACE inhibitor-associated angioedema and 489 ACE inhibitor-exposed controls from Nashville (Tennessee) and Marshfield (Wisconsin). We tested for replication in 19 cases and 57 controls who participated in Ongoing Telmisartan Alone and in Combination with Ramipril Global Endpoint Trial (ONTARGET).

Results: There were no genome-wide significant associations of any single-nucleotide polymorphism (SNP) with angioedema. Sixteen SNPs in African Americans and 41 SNPs in European Americans were associated moderately with angioedema (P<10) and evaluated for association in ONTARGET. The T allele of rs500766 in PRKCQ was associated with a reduced risk, whereas the G allele of rs2724635 in ETV6 was associated with an increased risk of ACE inhibitor-associated angioedema in the Nashville/Marshfield sample and ONTARGET. In a candidate gene analysis, rs989692 in the gene encoding neprilysin (MME), an enzyme that degrades bradykinin and substance P, was significantly associated with angioedema in ONTARGET and Nashville/Marshfield African Americans.

Conclusion: Unlike other serious adverse drug effects, ACE inhibitor-associated angioedema is not associated with a variant with a large effect size. Variants in MME and genes involved in immune regulation may be associated with ACE inhibitor-associated angioedema.
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http://dx.doi.org/10.1097/FPC.0b013e328363c137DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904664PMC
September 2013